What high-paying jobs will there be for data scientists in the future?

Experts in the field of Data science predict that the scenario will soon change in the future in some companies and industries that are less technology-oriented than others, where data science plays a supporting (or back-office) role in the company today, as these companies and industries are ready to access a huge amount of data - both structured and unstructured - as well as feedback loops.

Data science plays an important role when it comes to high-paying jobs of the future. To understand what's driving this trend, which experts say will only get bigger over time, it's important to understand that the technology landscape has experienced tremendous growth and fundamental change in recent years, thanks to high reliability of computer programs, data-based analysis and digital technology.

In particular, working with massive amounts of data and making data-driven decisions has become the norm across most sectors - from banking and fintech to transportation, IT and more. After all, almost every interaction with technology has data at its core. Be it your online shopping on marketplaces like Amazon, streaming recommendations for you on Netflix or Amazon Prime, your Facebook feed or even the facial recognition necessary to log in to your phone - almost everything uses data to carry out certain operations to carry out.

Experts in the field of data science predict that the scenario will soon change in the future in some companies and industries that are less technology-oriented than others, where data science now plays a supporting (or back-office) role in the company , as these companies and industries are poised to access a massive amount of data - both structured and unstructured - as well as feedback loops. And that's exactly what makes the job of data scientist one of the most sought-after in today's world.

Before we talk about the high-paying future jobs one can pursue, let's take a look at why the data scientist profession is so popular.

1- What makes the data scientist profession so popular?

In recent years, it has been noticed that people are not only taking courses and enrolling in bootcamps to be job ready, but may even be considering a career change as becoming a data scientist offers them a fat salary package and a lot of prestige would bring in.

For 2019, data scientists are ranked as the No. 1 most promising job in the U.S., according to a LinkedIn report. For this report, LinkedIn examined data from millions of job postings, member profiles and salaries and used five factors to rank top positions.

These factors were career progression, salary, number of vacancies in the US, their extensive regional availability and the annual increase in vacancies.

Even on Glassdoor's list of the best jobs in America, data scientist has been at the top for the past three years, which is consistent with what professionals say about the field - high salaries, high demand, and high job satisfaction.

Those looking for a well-paying salary should not think twice before taking a data scientist job as these professionals enjoy an average base salary of $130,000 and have 56% more job offers this year than in, according to the LinkedIn report Previous year. There are currently over 4,000 open data scientist positions across the United States.

2- A prediction about the type of well-paying future jobs in data science

Apart from working as a data scientist, professionals in the field of data science can also take up high-paying future jobs such as: E.g. Data Architect, Big Data Engineer, Database Manager, Business Intelligent Analyst, etc. All these jobs are paid with the best salaries in the industry and help companies make good decisions by using their data science skills to analyze large amounts of data and to draw useful insights from it.

Let's take a closer look at what each of the above careers would entail.

2.1- Data Architect

A data scientist who works as a data expert helps maintain and design data and its structure, which is constantly changing, while developing strategies and efficiency models and working on continuous design improvements with a strong emphasis on visualization.

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2.2- Big Data Engineer

This professional is responsible for examining massive amounts of data and manipulating it to produce valuable insights that business owners and executives can rely on to improve their business operations. As a big data engineer, you analyze, collect, report, and process the company's incoming and outgoing data. You will also oversee software, hardware and data infrastructure to ensure information is used effectively and optimized.

2.3- Database Manager

This professional is responsible for maintaining a company's databases and takes care of any problems that arise by diagnosing them and fixing them as quickly as possible. It is also the job of database managers to manage the database and its hardware, ensuring that the databases are up to date and compatible with newer software and systems.

2.4- Business Intelligence-Analyst

A data scientist working as a business intelligence analyst is responsible for evaluating business data and transforming it into useful information that executives and management/director-level employees can rely on to improve their business operations through data-driven decisions. Your responsibilities will also include producing data reports that are independent and highlight patterns, trends and modernization processes that can improve operational protocols.

2.5- Data Analysis Manager

Several companies are increasingly relying on these professionals to make sense of the data and communicate it to the rest of the team, with a focus on understanding how companies need to act on the insights gleaned from the data. Data scientists with a solid analytical and business background as well as management skills are the ideal candidates for the position of data analysis manager.

To understand the true nature of the high-paying future jobs for data scientists, it is important to know what the data science landscape will look like in the future. Dan Wulin - Head of Data Science and Machine Learning at Wayfair - provides a glimpse into that landscape, although he says he could be wrong in his predictions.

Dan Wulin identifies three overarching trends that he believes will shape the future of the data science landscape and therefore impact the types of well-paying jobs data scientists will be able to take in the future.

Increasingly complex data science algorithms will be included in technologies and packages that make their deployment easier: To better understand the impact, you can simply compare the experience of training and deploying algorithms like Random Forest in today's world , which was the case 10 years ago. Today the application is orders of magnitude faster and can be carried out with less statistical and technical knowledge, albeit with a higher level of quality. This has emerged as a general trend across many areas of data science and will continue to grow.

Adopting ML, AI, and related techniques: As more companies look to gain more and better insights from the data they collect and their partners, hiring data scientists and other data science professionals will become increasingly important. At the same time, companies will continue to use machine learning (ML), artificial intelligence (AI), and related techniques in ways that positively impact their business in fundamental ways.

The type of work data scientists do today will change, shifting it to workers trained in statistics and coding but less highly skilled: to keep up with market demand and the general shift in industries towards machine learning, AI, etc. To keep pace, students in academic courses are becoming more and more familiar with statistics, engineering, machine learning and linear algebra. By encouraging and incentivizing their students to develop appropriate technical skills, these academic programs will prepare students better for the job market than they are today. This, in turn, would mean that much of the work done by today's data scientists will ultimately be transferred to less well-trained individuals who have sufficient statistical and programming skills to successfully leverage robust packages and technologies and build machine learning models create.

As a result of points 2 and 3 above, the roles of data scientists would change in the future. One path would lead to high-paying future jobs that are somewhat similar to the jobs done by today's data science teams, who do extremely research-oriented work that goes beyond the techniques typically available. An example would be the application of machine learning at a deep level, similar to how it is used today in various use cases such as image classification, autonomous driving, etc.

The second path would lead to high-paying future jobs dealing with performing management tasks on the business side, for which companies now typically hire MBAs. As data science faces rapid and widespread growth in the coming years, there is increasing emphasis on ensuring that the field's fundamental techniques can be meaningfully linked to business problems. Therefore, there will be a growing demand for technically trained individuals who have solid communication skills and a sound business acumen. This means that data scientists who have these skills would be best positioned to take advantage of the new opportunities that will open up in the future.